How to Successfully Adjust to Retirement? Examining the Role of Pre-Retirement Resources
Bibliographic record
Abstract
Successfully adjusting to retirement represents a major challenge for many older workers. Although studies emphasize that successfully adjusting to new life circumstances in retirement may depend on the availability and fluctuation of specific resources, little is known about the impact of multiple pre-retirement resources availability and change on two distinctive outcomes: the process of successfully adjusting to retirement and, subsequently, the outcomes of such process in terms of post-retirement well-being. The current study draws from retirement adjustment resource-based dynamic theory to argue that multiple pre-retirement resources availability and change facilitate the process through which retirees get used to their new retirement life (retirement adjustment process), and, subsequently, their post-retirement well-being levels and change (retirement adjustment quality). Using archival data from 667 Chinese older workers transitioning into retirement collected with prospective longitudinal research design, we found evidence for positive impacts of multiple types of pre-retirement resources and their latent changes (i.e., financial well-being, family support, and proactive personality) on retirement adjustment process, which was in turn positively associated with post-retirement life satisfaction and its change. Further mediation tests revealed that the indirect effects through retirement adjustment process were statistically significant. The theoretical and practical implications of these findings are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".